How to avoid scientific notation when exporting mixed-type DataFrame to CSV in Pandas?

Other Questions Notion
1

I’m working with a Pandas DataFrame that has both float and string columns. When I use the to_csv function to save it, large numbers are converted to scientific notation. For instance, 1344154454156.992676 becomes 1.344154e+12 in the CSV file.

I’ve tried using the float_format parameter, but it doesn’t work because of the string columns. Here’s a small example of what I’m dealing with:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'text': ['apple', 'banana', 'cherry'],
    'big_num': np.random.rand(3) * 1e14
})

df.to_csv('output.csv')

The CSV output shows scientific notation for the ‘big_num’ column. How can I keep the full number representation for the float values while still handling the string column correctly? I’d like the output to look something like this:

  text                big_num
0 apple 94184321380806.796875
1 banana 22383735919307.046875
2 cherry 99180119890642.859375

Any suggestions on how to achieve this?

2

I’ve encountered this issue before when working with financial data. Here’s a solution that worked for me:

Use the float_format parameter in combination with a custom formatter function. This approach allows you to handle both float and string columns effectively.

def format_float(x):
    if isinstance(x, float):
        return f'{x:.6f}'
    return x

df.to_csv('output.csv', float_format='%.6f', formatters={'text': format_float})

This method preserves full precision for float values while keeping string columns intact. The formatters parameter applies the custom function only to the ‘text’ column, ensuring it’s not affected by float formatting.

Remember to adjust the number of decimal places in both the float_format and the custom function to suit your specific needs. This approach has been reliable in my experience, especially when dealing with mixed data types in large datasets.

3

I’ve dealt with this exact issue in my data analysis work. A straightforward solution is to use the ‘float_format’ parameter in combination with the ‘dtype’ parameter when writing to CSV. Here’s how you can do it:

df.to_csv(‘output.csv’, float_format=‘%.6f’, dtype={‘big_num’: str})

This approach forces Pandas to treat the ‘big_num’ column as a string, preserving its full representation, while still allowing you to control the decimal precision for other float columns. It’s a clean solution that doesn’t require any additional libraries or custom functions.

Just remember to adjust the ‘%.6f’ format specifier to match your desired precision. This method has consistently worked well for me across various projects with mixed data types.

4

hey, i had a similar problem. try using the ‘numpy’ library. you can convert the float column to a string with full precision like this:

import numpy as np
df[‘big_num’] = df[‘big_num’].apply(lambda x: np.format_float_positional(x, trim=‘-’))

then just export normally. worked for me!